library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.0.2
ggplot(cars)
ggplot(cars) + aes(x = speed, y = dist)
ggplot(cars) + aes(x = speed, y = dist) + geom_point()
ggplot(cars) + aes(x = speed, y = dist) + geom_point()+geom_smooth()
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
ggplot(cars) + aes(x = speed, y = dist) + geom_point()+geom_smooth() + theme_bw() + labs(title = "Speed Distance Relationship Between Cars", subtitle = "Random survey 2020")
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
ggplot(cars) + aes(x = speed, y = dist) + geom_point()+geom_smooth() + theme_bw() + labs(title = "Speed Distance Relationship Between Cars", subtitle = "Random survey 2020", x = "Speed (mph)", y = "Distance (m)", caption = "dataset :cars")
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
url <- "https://bioboot.github.io/bimm143_S20/class-material/up_down_expression.txt"
genes <- read.delim(url)
head(genes)
## Gene Condition1 Condition2 State
## 1 A4GNT -3.6808610 -3.4401355 unchanging
## 2 AAAS 4.5479580 4.3864126 unchanging
## 3 AASDH 3.7190695 3.4787276 unchanging
## 4 AATF 5.0784720 5.0151916 unchanging
## 5 AATK 0.4711421 0.5598642 unchanging
## 6 AB015752.4 -3.6808610 -3.5921390 unchanging
nrow(genes)
## [1] 5196
ncol(genes)
## [1] 4
colnames(genes)
## [1] "Gene" "Condition1" "Condition2" "State"
summary(genes)
## Gene Condition1 Condition2 State
## Length:5196 Min. :-3.6809 Min. :-3.5921 Length:5196
## Class :character 1st Qu.:-3.6809 1st Qu.:-3.5921 Class :character
## Mode :character Median :-0.9439 Median :-0.8552 Mode :character
## Mean : 0.1800 Mean : 0.2796
## 3rd Qu.: 4.0859 3rd Qu.: 4.0437
## Max. :13.1733 Max. :12.8731
table(genes$state)
## < table of extent 0 >
ggplot(data = genes) + aes(x = Condition1,y = Condition2) + geom_point()
p <- ggplot(data = genes) + aes(x = Condition1,y = Condition2, col = State) + geom_point()
p
p<-p + scale_color_manual(values = c("blue", "gray", "red"))
p
p <- p + labs(title = "Gene Expression Changes Upon Drug Treatment", y = "Drug Treatment", x = "Control (no drug)"
)
p
library(gapminder)
## Warning: package 'gapminder' was built under R version 4.0.2
library(dplyr)
## Warning: package 'dplyr' was built under R version 4.0.2
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
gapminder_2007 <- gapminder %>% filter(year == 2007)
ggplot(gapminder_2007) + aes(y = lifeExp, x = gdpPercap) + geom_point()
ggplot(gapminder_2007) + aes(y = lifeExp, x = gdpPercap) + geom_point(alpha = 0.4)
ggplot(gapminder_2007) + aes(y = lifeExp, x = gdpPercap, color = continent, size = pop) + geom_point(alpha = 0.4)
ggplot(gapminder_2007) + aes(y = lifeExp, x = gdpPercap, color = pop) + geom_point(alpha = 0.8)
ggplot(gapminder_2007) + aes(y = lifeExp, x = gdpPercap, size = pop) + geom_point(alpha = 0.4)
ggplot(gapminder_2007) + aes(y = lifeExp, x = gdpPercap, size = pop) + geom_point(alpha = 0.4) + scale_size_area(max_size = 10)
gapminder_1957 <- gapminder %>% filter(year == 1957)
ggplot(gapminder_1957) + aes(x = gdpPercap, y = lifeExp, color = continent, size = pop) + scale_size_area(max_size = 15) + geom_point(alpha = 0.7)
gapminder_1957 <- gapminder %>% filter(year == 1957 | year == 2007)
ggplot(gapminder_1957) + aes(x = gdpPercap, y = lifeExp, color = continent, size = pop) + scale_size_area(max_size = 15) + geom_point(alpha = 0.7) + facet_wrap(~year)
gapminder_top5 <- gapminder %>% filter(year == 2007) %>% arrange(desc(pop)) %>% top_n(5, pop)
gapminder_top5
## # A tibble: 5 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 China Asia 2007 73.0 1318683096 4959.
## 2 India Asia 2007 64.7 1110396331 2452.
## 3 United States Americas 2007 78.2 301139947 42952.
## 4 Indonesia Asia 2007 70.6 223547000 3541.
## 5 Brazil Americas 2007 72.4 190010647 9066.
ggplot(gapminder_top5) + geom_col(aes(x = country, y= pop))
ggplot(gapminder_top5) + geom_col(aes(x = country, y = lifeExp))
ggplot(gapminder_top5) + geom_col(aes(x = country, y = lifeExp, fill = continent))
ggplot(gapminder_top5) + geom_col(aes(x = country, y= pop, fill = continent))
ggplot(gapminder_top5) + geom_col(aes(x = country, y= pop, fill = lifeExp))
ggplot(gapminder_top5) + geom_col(aes(x = country, y= pop, fill = gdpPercap))
ggplot(gapminder_top5) + geom_col(aes(x = reorder(country, -pop), y= pop, fill = gdpPercap))
ggplot(gapminder_top5) + geom_col(aes(x = reorder(country, -pop), y= pop, fill = country), col = "gray30") + guides(fill = FALSE)
## Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> =
## "none")` instead.
head(USArrests)
## Murder Assault UrbanPop Rape
## Alabama 13.2 236 58 21.2
## Alaska 10.0 263 48 44.5
## Arizona 8.1 294 80 31.0
## Arkansas 8.8 190 50 19.5
## California 9.0 276 91 40.6
## Colorado 7.9 204 78 38.7
USArrests$State <- rownames(USArrests)
ggplot(USArrests) + aes(x = reorder(State, Murder), y = Murder) + geom_col() + coord_flip()
ggplot(USArrests) + aes(x = reorder(State, Murder), y = Murder) + geom_point() + geom_segment(aes(x = State, xend = State, y = 0, yend = Murder), color = "blue") + coord_flip()
library(gapminder)
library(gifski)
## Warning: package 'gifski' was built under R version 4.0.2
library(png)
## Warning: package 'png' was built under R version 4.0.2
library(gganimate)
## Warning: package 'gganimate' was built under R version 4.0.2
ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, colour = country)) +
geom_point(alpha = 0.7, show.legend = FALSE) +
scale_colour_manual(values = country_colors) +
scale_size(range = c(2, 12)) +
scale_x_log10() +
facet_wrap(~continent) +
labs(title = 'Year: {frame_time}', x = 'GDP per capita', y = 'life expectancy') +
transition_time(year) +
shadow_wake(wake_length = 0.1, alpha = FALSE)
library(patchwork)
## Warning: package 'patchwork' was built under R version 4.0.2
p1 <- ggplot(mtcars) + geom_point(aes(mpg, disp))
p2 <- ggplot(mtcars) + geom_boxplot(aes(gear, disp, group = gear))
p3 <- ggplot(mtcars) + geom_smooth(aes(disp, qsec))
p4 <- ggplot(mtcars) + geom_bar(aes(carb))
(p1 | p2 | p3) /
p4
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
sessionInfo()
## R version 4.0.0 (2020-04-24)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS 10.16
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] patchwork_1.1.1 gganimate_1.0.7 png_0.1-7 gifski_1.4.3-1
## [5] dplyr_1.0.7 gapminder_0.3.0 ggplot2_3.3.5
##
## loaded via a namespace (and not attached):
## [1] progress_1.2.2 tidyselect_1.1.1 xfun_0.29 purrr_0.3.4
## [5] splines_4.0.0 lattice_0.20-45 colorspace_2.0-2 vctrs_0.3.8
## [9] generics_0.1.0 htmltools_0.5.2 yaml_2.2.1 mgcv_1.8-37
## [13] utf8_1.2.2 rlang_0.4.11 jquerylib_0.1.4 pillar_1.6.3
## [17] glue_1.4.2 withr_2.4.2 DBI_1.1.1 tweenr_1.0.2
## [21] plyr_1.8.6 lifecycle_1.0.1 stringr_1.4.0 munsell_0.5.0
## [25] gtable_0.3.0 evaluate_0.14 labeling_0.4.2 knitr_1.36
## [29] fastmap_1.1.0 fansi_0.5.0 highr_0.9 Rcpp_1.0.7
## [33] scales_1.1.1 farver_2.1.0 hms_1.1.1 digest_0.6.28
## [37] stringi_1.7.5 grid_4.0.0 cli_3.0.1 tools_4.0.0
## [41] magrittr_2.0.1 tibble_3.1.5 crayon_1.4.1 pkgconfig_2.0.3
## [45] ellipsis_0.3.2 Matrix_1.3-4 prettyunits_1.1.1 assertthat_0.2.1
## [49] rmarkdown_2.11 rstudioapi_0.13 R6_2.5.1 nlme_3.1-153
## [53] compiler_4.0.0